Distributed Optimization for Network Resource Allocation With Nonsmooth Utility Functions
Distributed Optimization for Network Resource Allocation With Nonsmooth Utility Functions
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DOI：
10.1109/tcns.2018.2889011
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发表时间：
2019-12-01
影响因子：
4.2
通讯作者：
Iiduka, Hideaki
中科院分区：
文献类型：
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作者：
Iiduka, Hideaki
网络效用最大化问题是在容量约束下最大化网络的整体效用的问题，其中网络中的每个源都有自己的私有非光滑凹效用函数（允许精确地建模真实效用），并且网络中的每个链路只有其容量约束。针对这一问题，提出了两种分布式优化算法：投影邻近算法和投影次梯度算法。这些算法可以实现的情况下，每个源试图通过使用其邻近算子或次微分，以最大限度地提高其效用，每个链路试图通过使用度量投影到其容量约束集，以满足其容量约束。收敛性分析表明，这些算法是足够的每个源找到最佳的资源分配。通过与现有的分散式网络流量控制算法的数值比较，证明了所提出的算法的收敛性，最优性和性能。
The network utility maximization problem is the problem of maximizing the overall utility of a network under capacity constraints, where each source in the network has its own private nonsmooth concave utility function (which allows the true utility to be modeled accurately) and each link in the network has only its capacity constraint. To solve this problem, two distributed optimization algorithms are proposed: a projected proximal algorithm and a projected subgradient algorithm. These algorithms can be implemented for the case that each source tries to maximize only its utility by using its proximity operator or subdifferential and each link tries to satisfy only its capacity constraint by using the metric projection onto its capacity constraint set. A convergence analysis indicates that these algorithms are sufficient for each source to find the optimal resource allocation. The convergence, optimality, and performance of the proposed algorithms are demonstrated through numerical comparisons with the existing decentralized network flow control algorithm.